app.py

import gradio as gr
#import gradio.helpers
import torch
import os
import base64
from glob import glob
from pathlib import Path
from typing import Optional

from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video
from PIL import Image

import uuid
import random
from huggingface_hub import login, hf_hub_download

#gradio.helpers.CACHED_FOLDER = '/data/cache'

SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')

HF_API_KEY = os.getenv('HF_API_KEY', '')
login(token=HF_API_KEY)

pipe = StableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid-xt-1-1",
    torch_dtype=torch.float16,
    variant="fp16"
)
pipe.to("cuda")
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
#pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)

max_64_bit_int = 2**63 - 1

def generate_video(
    secret_token: str,
    image: Image,
    seed: int,
    motion_bucket_id: int = 127,
    fps_id: int = 6,
    version: str = "svd_xt",
    cond_aug: float = 0.02,
    decoding_t: int = 3,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
    device: str = "cuda",
    output_folder: str = "outputs",
):
    if secret_token != SECRET_TOKEN:
        raise gr.Error(
            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')


    # note julian: normally we should resize input images, but normally they are already in 1024x576, so..
    # also, I would like to experiment with vertical videos, and 1024x512 videos
    image = resize_image(image)
    
    if image.mode == "RGBA":
        image = image.convert("RGB")
        
    generator = torch.manual_seed(seed)
    
    os.makedirs(output_folder, exist_ok=True)
    base_count = len(glob(os.path.join(output_folder, "*.mp4")))
    video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")

    frames = pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=25).frames[0]
    export_to_video(frames, video_path, fps=fps_id)
    torch.manual_seed(seed)

    # Read the content of the video file and encode it to base64
    with open(video_path, "rb") as video_file:
        video_base64 = base64.b64encode(video_file.read()).decode('utf-8')

    # Prepend the appropriate data URI header with MIME type
    video_data_uri = 'data:video/mp4;base64,' + video_base64
    
    # clean-up (otherwise there is a risk of "ghosting", eg. someone seeing the previous generated video",
    # of one of the steps go wrong)
    os.remove(video_path)
    
    return video_data_uri

def resize_image(image, output_size=(1024, 576)):
    # Calculate aspect ratios
    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
    image_aspect = image.width / image.height  # Aspect ratio of the original image

    # Resize then crop if the original image is larger
    if image_aspect > target_aspect:
        # Resize the image to match the target height, maintaining aspect ratio
        new_height = output_size[1]
        new_width = int(new_height * image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        # Calculate coordinates for cropping
        left = (new_width - output_size[0]) / 2
        top = 0
        right = (new_width + output_size[0]) / 2
        bottom = output_size[1]
    else:
        # Resize the image to match the target width, maintaining aspect ratio
        new_width = output_size[0]
        new_height = int(new_width / image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        # Calculate coordinates for cropping
        left = 0
        top = (new_height - output_size[1]) / 2
        right = output_size[0]
        bottom = (new_height + output_size[1]) / 2

    # Crop the image
    cropped_image = resized_image.crop((left, top, right, bottom))
    return cropped_image

with gr.Blocks() as demo:
    secret_token = gr.Text(
        label='Secret Token',
        max_lines=1,
        placeholder='Enter your secret token')
    gr.HTML("""
        <div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;">
        <div style="text-align: center; color: black;">
        <p style="color: black;">This space is a REST API to programmatically generate MP4 videos.</p>
        <p style="color: black;">Interested in using it? Look no further than the <a href="https://huggingface.co/spaces/multimodalart/stable-video-diffusion" target="_blank">original space</a>!</p>
        </div>
        </div>""")
    image = gr.Image(label="Upload your image", type="pil")
    generate_btn = gr.Button("Generate")
    base64_out = gr.Textbox(label="Base64 Video")
    seed = gr.Slider(label="Seed", value=42, randomize=False, minimum=0, maximum=max_64_bit_int, step=1)
    motion_bucket_id = gr.Slider(label="Motion bucket id", info="Controls how much motion to add/remove from the image", value=127, minimum=1, maximum=255)
    fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=6, minimum=5, maximum=30)
       
    generate_btn.click(
        fn=generate_video,
        inputs=[secret_token, image, seed, motion_bucket_id, fps_id],
        outputs=base64_out,
        api_name="run")

demo.queue(max_size=20).launch()